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Daniel Guggenheim School of Aerospace Engineering
Daniel Guggenheim School of Aerospace Engineering
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ItemVision-Aided Inertial Navigation for Flight Control(Georgia Institute of Technology, 2005-09) Wu, Allen D. ; Johnson, Eric N. ; Proctor, Alison A.Many onboard navigation systems use the Global Positioning System to bound the errors that result from integrating inertial sensors over time. Global Positioning System information, however, is not always accessible since it relies on external satellite signals. To this end, a vision sensor is explored as an alternative for inertial navigation in the context of an Extended Kalman Filter used in the closed-loop control of an unmanned aerial vehicle. The filter employs an onboard image processor that uses camera images to provide information about the size and position of a known target, thereby allowing the flight computer to derive the target's pose. Assuming that the position and orientation of the target are known a priori, vehicle position and attitude can be determined from the fusion of this information with inertial and heading measurements. Simulation and flight test results verify filter performance in the closed-loop control of an unmanned rotorcraft.
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Item6-DOF Nonlinear Simulation of Vision-based Formation Flight(Georgia Institute of Technology, 2005-08) Sattigeri, Ramachandra J. ; Calise, Anthony J. ; Kim, Byoung Soo ; Volyanskyy, Konstantin ; Kim, NakwanThis paper presents an adaptive guidance and control law algorithm for implementation on a pair of Unmanned Aerial Vehicles (UAVs) in a 6 DOF leader-follower formation flight simulation. The objective of the simulation study is to prepare for a flight test involving a pair of UAVs in formation flight where the follower aircraft will be equipped with an onboard camera to estimate the relative distance and orientation to the leader aircraft. The follower guidance law is an adaptive acceleration based guidance law designed for the purpose of tracking a maneuvering leader aircraft. We also discuss the limitations of a preceding version of the guidance algorithm shown in a previous paper. Finally, we discuss the design of an adaptive controller (autopilot) to track the commands from the guidance algorithm. Simulation results for different leader maneuvers are presented and analyzed.
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ItemAdaptive Control for a Microgravity Vibration Isolation System(Georgia Institute of Technology, 2005-08) Yang, Bong-Jun ; Calise, Anthony J. ; Craig, James I. ; Whorton, Mark S.Most active vibration isolation systems that try to a provide quiescent acceleration environment for space-science experiments have utilized linear design methods. In this paper, we address adaptive control augmentation of an existing classical controller that combines a high-gain acceleration inner-loop feedback together with a low-gain position outer-loop feedback to regulate the platform about its center position. The control design considers both parametric and dynamic uncertainties because the isolation system must accommodate a variety of payloads having different inertial and dynamic characteristics. An important aspect of the design is the accelerometer bias. Two neural networks are incorporated to adaptively compensate for the uncertainties within the acceleration and the position loop. A novel feature in the design is that high-band pass and low pass filters are applied to the error signal used to adapt the weights in the neural network and the adaptive signals, so that the adaptive processes operate over targeted ranges of frequency. This prevents the inner and outer loop adaptive processes from interfering with each other. Simulations show that adaptive augmentation improves the performance of the existing acceleration controller and at the same time reduces the maximal position deviation and thus also improves the position controller.
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ItemEstimation and Guidance Strategies for Vision-based Target Tracking(Georgia Institute of Technology, 2005-06) Calise, Anthony J. ; Johnson, Eric N. ; Sattigeri, Ramachandra J. ; Watanabe, Yoko ; Madyastha, VenkateshThis paper discusses estimation and guidance strategies for vision-based target tracking. Specific applications include formation control of multiple unmanned aerial vehicles (UAVs) and air-to-air refueling. We assume that no information is communicated between the aircraft, and only passive 2-D vision information is available to maintain formation. To improve the robustness of the estimation process with respect to unknown target aircraft acceleration, the nonlinear estimator (EKF) is augmented with an adaptive neural network (NN). The guidance strategy involves augmenting the inverting solution of nonlinear line-of-sight (LOS) range kinematics with the output of an adaptive NN to compensate for target aircraft LOS velocity. Simulation results are presented that illustrate the various approaches.
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ItemAdaptive Trajectory Control for Autonomous Helicopters(Georgia Institute of Technology, 2005) Johnson, Eric N. ; Kannan, Suresh K.For autonomous helicopter flight, it is common to separate the flight control problem into an inner loop that controls attitude and an outer loop that controls the translational trajectory of the helicopter. In previous work, dynamic inversion and neural-network-based adaptation was used to increase performance of the attitude control system and the method of pseudocontrol hedging (PCH) was used to protect the adaptation process from actuator limits and dynamics. Adaptation to uncertainty in the attitude, as well as the translational dynamics, is introduced, thus, minimizing the effects of model error in all six degrees of freedom and leading to more accurate position tracking. The PCH method is used in a novel way that enables adaptation to occur in the outer loop without interacting with the attitude dynamics. A pole-placement approach is used that alleviates timescale separation requirements, allowing the outer-loop bandwidth to be closer to that of the inner loop, thus, increasing position tracking performance. A poor model of the attitude dynamics and a basic kinematics model is shown to be sufficient for accurate position tracking. The theory and implementation of such an approach, with a summary of flight-test results, are described.
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ItemNeural-Network Augmentation of Existing Linear Controllers(Georgia Institute of Technology, 2005-01) Sharma, Manu ; Calise, Anthony J.A method to augment existing linear controllers with a multilayer neural network is presented. The neural network is adapted online to ensure desired closed-loop response in the face of parametric plant uncertainty; no off-line training is required. The benefit of this scheme is that the neural-network output is simply added to the nominal control signal, thereby preserving the existing control architecture. Furthermore, the nominal control signal is only modified if the desired closed-loop response is not met. This method applies to a large class of modern and classical linear controllers. Stability guarantees are provided via Lyapunov-like analysis, and the efficacy of this scheme is illustrated through two numerical examples.
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ItemAdaptive Control for a Microgravity Vibration Isolation System(Georgia Institute of Technology, 2005) Yang, Bong-Jun ; Calise, Anthony J. ; Craig, James I. ; Whorton, Mark S.Most active vibration isolation systems that try to a provide quiescent acceleration environment for space-science experiments have utilized linear design methods. In this report, we address adaptive control augmentation of an existing classical controller that combines a high-gain acceleration inner-loop feedback together with a low-gain position outer-loop feedback to regulate the platform about its center position. The control design considers both parametric and dynamic uncertainties because the isolation system must accommodate a variety of payloads having different inertial and dynamic characteristics. We show how adaptive control is beneficial in three important aspects in design of a controller for uncertain systems: performance, robustness, and transient responses. First, performance is treated in the setting that an accelerometer and an actuator is located at the same location, as is the current hardware configuration for g-LIMIT. Second, robustness for the control system becomes more of an issue when the sensor is non-collocated with the actuator. We illustrate that adaptive control can stabilize otherwise unstable dynamics due to the presence of unmodelled dynamics. Third, transient responses of the position of the isolation system are significantly influenced by a high-gain acceleration controller when it includes integral action. An important aspect of the g-LIMIT is the accelerometer bias and the deviation of the platform it causes as a result of integral control. By employing adaptive neural networks for both the inner-loop and outer-loop controllers, we illustrate that adaptive control can improve both steady-state responses and transient responses in position. A feature in the design is that high-band pass and low pass filters are applied to the error signal used to adapt the weights in the neural network and the adaptive signals, so that the adaptive processes operate over targeted ranges of frequency. This prevents the inner and outer loop adaptive processes from interfering with each other.
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ItemVisual Search Automation for Unmanned Aerial Vehicles(Georgia Institute of Technology, 2005-01) Johnson, Eric N. ; Proctor, Alison A. ; Ha, Jin-Cheol ; Tannenbaum, Allen R.This paper describes the design, development, and testing of an Unmanned Aerial Vehicle (UAV) with automated capabilities: searching a prescribed area, identifying a specific building within that area based on a small sign located on one wall, and then identifying an opening into that building. This includes a description of the automated search system along with simulation and flight test results. Results include successful evaluation at the McKenna Military Operations in Urban Terrain flight test site.